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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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, if any, must be included) – Certified copy of Academic Degree/s in original language along with a certified translation into English, and/or Diploma Supplement (if applicable) – Certified copies
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have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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Division Macroeconomic Forecasting and Data Science analyses and forecasts the Swiss and international economy and produces KOF’s short- and medium-term macroeconomic outlooks using macroeconometric models
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for FGT 3.0: confidential HPC pipelines, scalable training infrastructure, and fine-tuned medical LLMs trained on clinical guidelines, evidence-based datasets, and real-world medical corpora. These models
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Field Theory Appl Deadline: 2025/04/27 11:59PM filled (posted 2025/03/31, listed until 2025/09/30) Position Description: Apply Position Description The Theoretical Particle Physics Laboratory (LPTP
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modification of primary human immune cells (T cells and macrophages). Conduct in vitro validations using advanced models, including patient-derived organoids and co-culture systems. Perform in vivo validations
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fundamental questions including: How can we best simulate Hermitian and non-Hermitian strongly correlated quantum systems and harness the power of both classical and quantum computing resources? How can we
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that autonomously optimizes 3D velocimetry measurements by dynamically adjusting camera positions and optical parameters. Integrating the framework within a digital twin environment for pre-training and simulation